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The CINDI Programme in Poland: Vision and Reality. A 25-year Story

2023· article· en· W6964934538 on OpenAlexaboutno aff

Bibliographic record

VenueCeON Repository (Centre for Evaluation in Education and Science) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)Health promotionPopulationIntervention (counseling)Public healthPopulation health

Abstract

fetched live from OpenAlex

The CINDI (Countrywide Integrated Noncommunicable Diseases Intervention) programme of the World Health Organization is one of the most famous long-term international intervention and research programmes focused on health promotion and prevention of chronic non-communicable diseases on a population scale. The origins of the CINDI Programme date back to the early 1990s, when on the initiative of the WHO European Office and a group of European countries interested in the prevention of noncommunicable diseases, several countries from the European region and Canada joined the international network. In Poland the CINDI initiatives were coordinated by the Departmentof Preventive and Social Medicine, Medical University of Lodz, and several other urban centres (among others Kalisz, Ostrów Wielkoposki, Chorzów, Toruń, Pabianice, Cieszyn, Włocławek, Przemyśl, Pleszew, Rawa Mazowiecka) joined the national progamme activities. This article presents the most important achievements of the CINDI programme, with particular emphasis on health monitoring activities, training of medical staff, and innovative educational and intervention programmes. The anti-tobacco campaign “Quit and Win”, the National Physical Activity Campaign “Revitalize Your Heart” as well as several representative population health surveys in Łódź and Toruń turned out to be particularly successful. Taking into account the social, medical and economic benefits resulting from the long-term activities of the CINDI programme, the authors emphasized the need to undertake further initiatives and out-lined the prospects for development of the programme on a nationaland international scale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.323
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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